Medical imaging techniques such as nuclear imaging, computerized tomography ("CT") and nuclear magnetic resonance imaging ("NMR") generate two-dimensional images from cross-sectional scans of patients. In nuclear imaging, for example, the image is generated from measurements tracing the position or movement of a radioactive isotope administered to the patient. Since certain radioactive isotopes accumulate in certain organs, it is possible to study these organs through the selection of an appropriate isotope.
The position or movement of the isotope is monitored by sensing the radioactive output from the isotope. After the isotope has been administered, the patient is monitored by detectors which detect radiation intensity. Each detector within an array monitors radioactive output from the patient, producing a set of intensity readings in the time domain representing a cross-section of interest within the patient. The subject cross-sections are frequently taken transversely, that is, normal to the long axis of the patient, in order to simplify the radiation detection equipment.
Many commercially available imaging systems use a five step process to reconstruct cross-sectional images. Raw time-domain data is extracted, that is, input and formatted into data views. The data from each view is then converted to a frequency domain by means of a fast fourier transform ("FFT") or other technique. The frequency domain data is then "filtered" to remove "noise" and sharpen the image. The filtered data is then converted back into the time domain, as by an inverse fast fourier transform, and backprojected over a cross-section to form an image. "Filtering" is essentially a weighted averaging process, in which the frequency domain data is subjected to convolution integration with a kernel referred to as a "filter." The filter is a vector in the frequency domain which augments one or more bands of frequencies likely to be of interest and attenuates others. Preferred types of filters have been developed over the years under names such as Butterworth, Hamming, Metz and Wiener filters. While the type of filter determines generally the shape of the filter, the details of the shape of the filter are determined by numerical parameters. The mathematical relations defining these filter types are well-known and available in reference books.
Care must be exercised in the selection of the filter. Filtering with too strong a filter, that is, a filter which is weighted too heavily in favor of selected frequencies, will tend to blur the image. On the other hand, filtering with too weak a filter will lead to a "noisy" image. The operator must select a proper filter based on his or her experience with images produced with the available equipment.
The amount of time-domain data, and hence the length of the frequency-domain data lines, is dependent on the actual size of the cross-sections and the structure of the array of radiation detectors. Different conditions may give rise to time-domain data sets, and hence frequency-domain data lines, of different length. As the length of the frequency-domain data lines varies, the filter must be adapted in order to discriminate between the frequencies of interest and those attributable to "noise."
Typical prior art systems offer a library of pre-defined filter functions for use within the reconstruction. A filter is selected prior to reconstruction and used throughout that processing session. If the pre-defined filter does not yield an image of the desired clarity, the operator must either select a different pre-defined filter or create a new one. Selection of a different pre-defined filter can be accomplished within the reconstruction application, but typically filter creation is a separate program and must be performed outside the reconstruction application. As a consequence, the image cannot be "fine-tuned" within the application to produce the desired image quality.